Acrosome evaluation of spermatozoa cells using sift and classical texture descriptors

• Autores: Laura Fernández Robles, Maite García-Ordás, Diego García-Ordás, Óscar García-Olalla Olivera, Enrique Alegre Gutiérrez
• Localización: Actas de las XXXII Jornadas de Automática, Escuela Técnica Superior de Ingeniería, Univesidad de Sevilla: Sevilla, 7 al 9 de septiembre de 2011, 2011, ISBN 978-84-694-6454-0, pág. 84
• Idioma: inglés
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• Resumen

  • Automatic assessment of sperm quality is an important challenge in the veterinary field. In this paper, we explore how to best describe the acrosomes of boar spermatozoa using image analysis to automatically classify them as intact or damaged.

    Our proposal is to characterize the acrosomes in terms of their membrane integrity using texture descriptors and compare them with descriptors based on local invariant features, particularly, Scale Invariant Feature Transform (SIFT) method. On the one hand, we use Zernike moments and Haralick features extracted from the original image and from the coefficients of the Discrete Wavelet Transform. On the other hand, the heads’ features are distinctively described by SIFT, a method based on detecting local points of interest.

    Classification using kNN shows that the best results were obtained by SIFT, with an overall hit rate of 84.64% and, what is more important, a higher hit rate in the damaged (92.96%) than in the intact class (76.15%). These results make this descriptor very attractive for the veterinary community.